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Found 17 papers, 2 papers with code
Distributionally Robust Policy and Lyapunov-Certificate Learning
This article presents novel methods for synthesizing distributionally robust stabilizing neural controllers and certificates for control systems under model uncertainty.
Optimal Power Flow Pursuit via Feedback-based Safe Gradient Flow
Numerical experiments on a 93-bus distribution system and with realistic load and production profiles show a superior performance in terms of voltage regulation relative to existing methods.
Online Regulation of Dynamical Systems to Solutions of Constrained Optimization Problems
This paper considers the problem of regulating a dynamical system to equilibria that are defined as solutions of an input- and state-constrained optimization problem.
Efficient Reconstruction of Neural Mass Dynamics Modeled by Linear-Threshold Networks
This paper studies the data-driven reconstruction of firing rate dynamics of brain activity described by linear-threshold network models.
Reinforcement Learning for Distributed Transient Frequency Control with Stability and Safety Guarantees
This paper proposes a reinforcement learning-based approach for optimal transient frequency control in power systems with stability and safety guarantees.
Learning Invariant Stabilizing Controllers for Frequency Regulation under Variable Inertia
Declines in cost and concerns about the environmental impact of traditional generation have boosted the penetration of renewables and non-conventional distributed energy resources into the power grid.
Agent-Supervisor Coordination for Decentralized Event-Triggered Optimization
This paper proposes decentralized resource-aware coordination schemes for solving network optimization problems defined by objective functions which combine locally evaluable costs with network-wide coupling components.
Encoding Impact of Network Modification on Controllability via Edge Centrality Matrix
This paper develops tools to quantify the importance of agent interactions and its impact on global performance metrics for networks modeled as linear time-invariant systems.
Iterative Algorithms for Assessing Network Resilience Against Structured Perturbations
This paper studies network resilience against structured additive perturbations to its topology.
Nesterov Acceleration for Equality-Constrained Convex Optimization via Continuously Differentiable Penalty Functions
The expressions of these Lagrange multiplier functions, which depend upon the gradients of the objective function and the constraints, can make the unconstrained penalty function non-convex in general even if the original problem is convex.
Optimization and Control Systems and Control Systems and Control
Structural Characterization of Oscillations in Brain Networks with Rate Dynamics
Exploiting the switched-affine nature of this dynamics, we obtain various necessary and/or sufficient conditions on the network structure and its external input for the existence of oscillations in (i) two-dimensional excitatory-inhibitory networks (E-I pairs), (ii) networks with one inhibitory but arbitrary number of excitatory nodes, (iii) purely inhibitory networks with an arbitrary number of nodes, and (iv) networks of E-I pairs.
Network Optimization via Smooth Exact Penalty Functions Enabled by Distributed Gradient Computation
This paper proposes a distributed algorithm for a network of agents to solve an optimization problem with separable objective function and locally coupled constraints.
Optimization and Control Multiagent Systems
Enabling DER Participation in Frequency Regulation Markets
In this paper, we propose a framework for collections of DERs, combined to form microgrids and controlled by aggregators, to participate in frequency regulation markets.
Frequency Regulation with Heterogeneous Energy Resources: A Realization using Distributed Control
This paper presents one of the first real-life demonstrations of coordinated and distributed resource control for secondary frequency response in a power distribution grid.
Data-Driven Distributed Predictive Control via Network Optimization
We consider a networked linear system where system matrices are unknown to the individual agents but sampled data is available to them.
Convergence-Rate-Matching Discretization of Accelerated Optimization Flows Through Opportunistic State-Triggered Control
A recent body of exciting work seeks to shed light on the behavior of accelerated methods in optimization via high-resolution differential equations.
On the detectability of transiting planets orbiting white dwarfs using LSST
White dwarfs are one of the few types of stellar objects for which we know almost nothing about the possible existence of companion planets.
Earth and Planetary Astrophysics
